Network analysis
We want to look at two networks:
- Where do people publish most often (bimodal)
- Who publishes with each other
Graph of authors and publications bimodal network, colored by author and publication.
Graph of authors and their co-authors, colored by gender. (NB: This isn’t working yet.)
Summary statistics
Number of journals that accounted for % of references.
Number of journals.
Number of conference proceedings.
Top three journal publications and their counts. ASEE at 32, Sex Roles at 21, and Journal of Women and Minorities in Science and Engineering at 21.
Top twenty journals that publish articles on women in STEM in higher education.
Number of publishers and percentage of journals they own.
Top publisher with top three journal titles
NB: The trick here is that there are ties in the dataset, and my code doesn’t account for those ties (it was pretty tricky to get this working). If necessary, I might be able to modify this to try and show all cases where there are ties. Sort by publisher to see the top publishers and their corresponding top three journal titles.
Percentage increase in journals published between 2007 and 2018.
Total percentage increase between 2007 and 2018 across proceedings and journals.
perc_increase_low <- data %>% filter(date == 2007) %>% count()
perc_increase_high <- data %>% filter(date == 2018) %>% count()
paste0(round(perc_increase_high/ perc_increase_low * 100, 2), "%")## [1] "2420%"